Undo last commit

frontend/app.py

@@ -22,7 +22,7 @@ import re
 from nltk.tokenize import word_tokenize
 from nltk.corpus import stopwords
 
-# --- Download NLTK resources if not already present ---
+# --- Download NLTK 'punkt' resource if not already present ---
 try:
     nltk.data.find('tokenizers/punkt')
 except LookupError:
@@ -33,7 +33,7 @@ try:
 except LookupError:
     nltk.download('stopwords')
 
-# --- Preprocess text function ---
+# --- Preprocess text function (moved outside session state) ---
 def preprocess_text(text):
     # Convert to lowercase
     text = text.lower()
@@ -51,104 +51,190 @@ def preprocess_text(text):
 
     return cleaned_text
 
-# --- Load necessary data for symptom detection ---
-dataset_1 = pd.read_csv("frontend/Symptoms_Detection/training_data.csv")
-dataset_1['symptoms_text'] = dataset_1.apply(lambda row: ','.join([col for col in dataset_1.columns if row[col] == 1]), axis=1)
-final_dataset = pd.DataFrame(dataset_1[["prognosis", "symptoms_text"]])
-final_dataset.columns = ['label', 'text']
-
-dataset_2 = pd.read_csv("frontend/Symptoms_Detection/Symptom2Disease.csv")
-dataset_2 = dataset_2[["label", "text"]]
-
-df_combined = pd.concat([final_dataset, dataset_2], axis=0, ignore_index=True)
-df_combined["cleaned_text"] = df_combined["text"].apply(preprocess_text)
-
-data = pd.read_csv('frontend/Symptoms_Detection/final_dataset_llms.csv')
-X = data['cleaned_text']
-y = data['label']
-
-vectorizer = CountVectorizer()
-X_vectorized = vectorizer.fit_transform(X)
-
-X_train, X_test, y_train, y_test = train_test_split(X_vectorized, y, test_size=0.2, random_state=42)
-
-model = LogisticRegression()
-model.fit(X_train, y_train)
-
-# --- Function to predict new symptoms ---
-def predict_symptoms(new_symptoms):
-    preprocessed_text = preprocess_text(new_symptoms)
-
-    if isinstance(preprocessed_text, str):
-        new_symptoms = [preprocessed_text]
-
-    # Vectorize the new symptoms
-    new_symptoms_vectorized = vectorizer.transform(new_symptoms)
-    # Make predictions
-    prediction = model.predict(new_symptoms_vectorized)
-
-    return prediction
-
-# --- Functions for precaution and occurrence ---
-def precaution(label):
-    dataset_precau = pd.read_csv("frontend/Symptoms_Detection/disease_precaution.csv", encoding='latin1')
-    label = str(label)
-    label = label.lower() 
-
-    dataset_precau["Disease"] = dataset_precau["Disease"].str.lower()
-    filtered_precautions = dataset_precau[dataset_precau["Disease"] == label]
-    precautions = filtered_precautions[["Precaution_1", "Precaution_2", "Precaution_3", "Precaution_4"]]
-    return precautions.values.tolist()
-
-def occurance(label):
-    dataset_occur = pd.read_csv("frontend/Symptoms_Detection/disease_riskFactors.csv", encoding='latin1')
-    label = str(label)
-    label = label.lower() 
-
-    dataset_occur["DNAME"] = dataset_occur["DNAME"].str.lower()
-    filtered_occurrence = dataset_occur[dataset_occur["DNAME"] == label]
-    occurrences = filtered_occurrence["OCCUR"].tolist()
-    return occurrences
-
-# --- Streamlit App ---
 st.title("Medi Scape Dashboard")
 
 # --- Session State Initialization ---
-# ... (rest of the session state initialization code)
-
-# --- Sidebar Navigation ---
-# ... (sidebar navigation code)
-
-# --- Main Content ---
-if page == "Home":
-    # ... (home page content)
-
-elif page == "AI Chatbot Diagnosis":
-    st.write("Enter your symptoms separated by commas:")
-    symptoms_input = st.text_area("Symptoms:")
-
-    col1, col2 = st.columns(2)
-
-    with col1:
-        if st.button("Diagnose (Regression Model)"):
-            if symptoms_input:
-                prediction = predict_symptoms(symptoms_input)
-                st.write("## Regression Model Prediction:")
-                st.write(prediction)
-                st.write("## Precautions:")
-                st.write(precaution(prediction[0]))
-                st.write("## Occurrence:")
-                st.write(occurance(prediction[0]))
-            else:
-                st.write("Please enter your symptoms.")
-
-    with col2:
-        if st.button("Diagnose (LLM)"):
+if 'disease_model' not in st.session_state:
+    try:
+        model_path = 'FINAL_MODEL.keras'  # Updated path
+        print(f"Attempting to load disease model from: {model_path}")
+        print(f"Model file exists: {os.path.exists(model_path)}") 
+        st.session_state.disease_model = tf.keras.models.load_model(model_path)
+        print("Disease model loaded successfully!")
+    except FileNotFoundError:
+        st.error("Disease classification model not found. Please ensure 'FINAL_MODEL.keras' is in the same directory as this app.")
+        st.session_state.disease_model = None
+
+# Load the vectorizer 
+if 'vectorizer' not in st.session_state:
+    try:
+        vectorizer_path = "vectorizer.pkl"
+        print(f"Attempting to load vectorizer from: {vectorizer_path}")
+        print(f"Vectorizer file exists: {os.path.exists(vectorizer_path)}")
+        st.session_state.vectorizer = pd.read_pickle(vectorizer_path)
+        print("Vectorizer loaded successfully!")
+    except FileNotFoundError:
+        st.error("Vectorizer file not found. Please ensure 'vectorizer.pkl' is in the same directory as this app.")
+        st.session_state.vectorizer = None
+    except Exception as e:
+        st.error(f"An error occurred while loading the vectorizer: {e}")
+        st.session_state.vectorizer = None
+
+if 'model_llm' not in st.session_state:
+    try:
+        llm_model_path = "frontend/logistic_regression_model.pkl"  # Updated path
+        print(f"Attempting to load LLM model from: {llm_model_path}")
+        print(f"LLM Model file exists: {os.path.exists(llm_model_path)}")
+        st.session_state.model_llm = pd.read_pickle(llm_model_path)  
+        print("LLM model loaded successfully!")
+    except FileNotFoundError:
+        st.error("LLM model file not found. Please ensure 'logistic_regression_model.pkl' is in the 'frontend' directory.")
+        st.session_state.model_llm = None
+    except Exception as e:
+        st.error(f"An error occurred while loading the LLM model: {e}")
+        st.session_state.model_llm = None
+
+# --- End of Session State Initialization ---
+
+# Load the disease classification model
+try:
+    disease_model = tf.keras.models.load_model('FINAL_MODEL.keras')  # Updated path
+except FileNotFoundError:
+    st.error("Disease classification model not found. Please ensure 'FINAL_MODEL.keras' is in the same directory as this app.")
+    disease_model = None
+
+# Sidebar Navigation
+st.sidebar.title("Navigation")
+page = st.sidebar.radio("Go to", ["Home", "AI Chatbot Diagnosis", "Drug Identification", "Disease Detection", "Outbreak Alert"])
+
+# Access secrets using st.secrets
+if "INFERENCE_API_URL" not in st.secrets or "INFERENCE_API_KEY" not in st.secrets:
+    st.error("Please make sure to set your secrets in the Streamlit secrets settings.")
+else:
+    # Initialize the Inference Client
+    CLIENT = InferenceHTTPClient(
+        api_url=st.secrets["INFERENCE_API_URL"],
+        api_key=st.secrets["INFERENCE_API_KEY"]
+    )
+
+    # Function to preprocess the image
+    def preprocess_image(image_path):
+        # Load the image
+        image = cv2.imread(image_path)
+
+        # Convert to grayscale
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+        # Remove noise
+        blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+
+        # Thresholding/Binarization
+        _, binary = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+
+        # Dilation and Erosion
+        kernel = np.ones((1, 1), np.uint8)
+        dilated = cv2.dilate(binary, kernel, iterations=1)
+        eroded = cv2.erode(dilated, kernel, iterations=1)
+
+        # Edge detection
+        edges = cv2.Canny(eroded, 100, 200)
+
+        # Deskewing
+        coords = np.column_stack(np.where(edges > 0))
+        angle = cv2.minAreaRect(coords)[-1]
+        if angle < -45:
+            angle = -(90 + angle)
+        else:
+            angle = -angle
+
+        (h, w) = edges.shape[:2]
+        center = (w // 2, h // 2)
+        M = cv2.getRotationMatrix2D(center, angle, 1.0)
+        deskewed = cv2.warpAffine(edges, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
+
+        # Find contours
+        contours, _ = cv2.findContours(deskewed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+        # Draw contours on the original image
+        contour_image = image.copy()
+        cv2.drawContours(contour_image, contours, -1, (0, 255, 0), 2)
+
+        return contour_image
+
+    def get_x1(detection):
+        return detection.xyxy[0][0]
+
+    # --- Prediction function (using session state) ---
+    def predict_disease(symptoms):
+        if st.session_state.vectorizer is not None and st.session_state.model_llm is not None:
+            preprocessed_symptoms = preprocess_text(symptoms)
+            symptoms_vectorized = st.session_state.vectorizer.transform([preprocessed_symptoms])
+            prediction = st.session_state.model_llm.predict(symptoms_vectorized)
+            return prediction[0]
+        else:
+            st.error("Unable to make prediction. Vectorizer or LLM model is not loaded.")
+            return None
+
+    # --- New function to analyze X-ray with LLM ---
+    def analyze_xray_with_llm(predicted_class):
+        prompt = f"""
+        Based on a chest X-ray analysis, the predicted condition is {predicted_class}. 
+        Please provide a concise summary of this condition, including:
+        - A brief description of the condition.
+        - Common symptoms associated with it.
+        - Potential causes.
+        - General treatment approaches.
+        - Any other relevant information for a patient.
+        """
+        llm_response = get_ai71_response(prompt)
+        st.write("## LLM Analysis of X-ray Results:")
+        st.write(llm_response)
+
+    if page == "Home":
+        st.markdown("## Welcome to Medi Scape")
+        st.write("Medi Scape is an AI-powered healthcare application designed to streamline the process of understanding and managing medical information.  It leverages advanced AI models to provide features such as prescription analysis, disease detection from chest X-rays, and symptom-based diagnosis assistance.")
+
+        st.markdown("## Features")
+        st.write("Medi Scape provides various AI-powered tools for remote healthcare, including:")
+        features = [
+            "**AI Chatbot Diagnosis:** Interact with an AI chatbot for preliminary diagnosis and medical information.",
+            "**Drug Identification:** Upload a prescription image to identify medications and access relevant details.",
+            "**Doctor's Handwriting Identification:** Our system can accurately recognize and process doctor's handwriting.",
+            "**Disease Detection:** Upload a chest X-ray image to detect potential diseases.",
+            "**Outbreak Alert:** Stay informed about potential disease outbreaks in your area."
+        ]
+        for feature in features:
+            st.markdown(f"- {feature}")
+
+        st.markdown("## How it Works")
+        steps = [
+            "**Upload:** You can upload a prescription image for drug identification or a chest X-ray image for disease detection.",
+            "**Process:** Our AI models will analyze the image and extract relevant information.",
+            "**Results:** You will receive identified drug names, uses, side effects, and more, or a potential disease diagnosis."
+        ]
+        for i, step in enumerate(steps, 1):
+            st.markdown(f"{i}. {step}")
+
+        st.markdown("## Key Features")
+        key_features = [
+            "**AI-Powered:** Leverages advanced AI models for accurate analysis and diagnosis.",
+            "**User-Friendly:** Simple and intuitive interface for easy navigation and interaction.",
+            "**Secure:** Your data is protected and handled with confidentiality."
+        ]
+        for feature in key_features:
+            st.markdown(f"- {feature}")
+
+        st.markdown("Please use the sidebar to navigate to different features.")
+
+    elif page == "AI Chatbot Diagnosis":
+        st.write("Enter your symptoms separated by commas:")
+        symptoms_input = st.text_area("Symptoms:")
+        if st.button("Diagnose"):
             if symptoms_input:
                 # --- Pipeline 1 Implementation ---
                 # 1. Symptom Input (already done with st.text_area)
-                # 2. Regression Prediction (using predict_disease function)
-                regression_prediction = predict_disease(symptoms_input)  # Assuming predict_disease function exists
+                # 2. Regression Prediction
+                regression_prediction = predict_disease(symptoms_input)
 
                 if regression_prediction is not None:
                     # 3. LLM Prompt Enhancement
@@ -170,13 +256,189 @@ elif page == "AI Chatbot Diagnosis":
             else:
                 st.write("Please enter your symptoms.")
 
-elif page == "Drug Identification":
-    # ... (drug identification page content)
+    elif page == "Drug Identification":
+        st.write("Upload a prescription image for drug identification.")
+        uploaded_file = st.file_uploader("Upload prescription", type=["png", "jpg", "jpeg"])
+
+        if uploaded_file is not None:
+            # Display the uploaded image
+            image = Image.open(uploaded_file)
+            st.image(image, caption="Uploaded Prescription", use_column_width=True)
 
-elif page == "Disease Detection":
-    # ... (disease detection page content)
+            if st.button("Process Prescription"):
+                # Save the image to a temporary file
+                temp_image_path = "temp_image.jpg"
+                image.save(temp_image_path)
 
-elif page == "Outbreak Alert":
-    # ... (outbreak alert page content)
-
-# ... (rest of the code)
+                # Preprocess the image
+                preprocessed_image = preprocess_image(temp_image_path)
+
+                # Perform inference
+                result_doch1 = CLIENT.infer(preprocessed_image, model_id="doctor-s-handwriting/1")
+
+                # Extract labels and detections
+                labels = [item["class"] for item in result_doch1["predictions"]]
+                detections = sv.Detections.from_inference(result_doch1)
+
+                # Sort detections and labels
+                sorted_indices = sorted(range(len(detections)), key=lambda i: get_x1(detections[i]))
+                sorted_detections = [detections[i] for i in sorted_indices]
+                sorted_labels = [labels[i] for i in sorted_indices]
+
+                # Convert list to string
+                resulting_string = ''.join(sorted_labels)
+
+                # Display results
+                st.subheader("Processed Prescription")
+                fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))
+
+                # Plot bounding boxes
+                image_with_boxes = preprocessed_image.copy()
+                for detection in sorted_detections:
+                    x1, y1, x2, y2 = detection.xyxy[0]
+                    cv2.rectangle(image_with_boxes, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 0), 2)      
+                ax1.imshow(cv2.cvtColor(image_with_boxes, cv2.COLOR_BGR2RGB))
+                ax1.set_title("Bounding Boxes")
+                ax1.axis('off')
+
+                # Plot labels
+                image_with_labels = preprocessed_image.copy()
+                for i, detection in enumerate(sorted_detections):
+                    x1, y1, x2, y2 = detection.xyxy[0]
+                    label = sorted_labels[i]
+                    cv2.putText(image_with_labels, label, (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+                ax2.imshow(cv2.cvtColor(image_with_labels, cv2.COLOR_BGR2RGB))
+                ax2.set_title("Labels")
+                ax2.axis('off')
+
+                st.pyplot(fig)
+
+                st.write("Extracted Text from Prescription:", resulting_string)
+
+                # Prepare prompt for LLM
+                prompt = f"""Analyze the following prescription text:
+                {resulting_string}
+
+                Please provide:
+                1. Identified drug name(s)
+                2. Full name of each identified drug
+                3. Primary uses of each drug
+                4. Common side effects
+                5. Recommended dosage (if identifiable from the text)
+                6. Any warnings or precautions
+                7. Potential interactions with other medications (if multiple drugs are identified)
+                8. Any additional relevant information for the patient
+
+                If any part of the prescription is unclear or seems incomplete, please mention that and provide information about possible interpretations or matches. Always emphasize the importance of consulting a healthcare professional for accurate interpretation and advice."""
+
+                # Get LLM response
+                llm_response = get_ai71_response(prompt)
+
+                st.subheader("AI Analysis of the Prescription")
+                st.write(llm_response)
+
+                # Remove the temporary image file
+                os.remove(temp_image_path)
+
+        else:
+            st.info("Please upload a prescription image to proceed.")
+
+    elif page == "Disease Detection":
+        st.write("Upload a chest X-ray image for disease detection.")
+        uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+        if uploaded_image is not None and st.session_state.disease_model is not None:
+            # Display the image
+            img_opened = Image.open(uploaded_image).convert('RGB')
+            image_pred = np.array(img_opened)
+            image_pred = cv2.resize(image_pred, (150, 150))
+
+            # Convert the image to a numpy array
+            image_pred = np.array(image_pred)
+
+            # Rescale the image (if the model was trained with rescaling)
+            image_pred = image_pred / 255.0
+
+            # Add an extra dimension to match the input shape (1, 150, 150, 3)
+            image_pred = np.expand_dims(image_pred, axis=0)
+
+            # Predict using the model
+            prediction = st.session_state.disease_model.predict(image_pred)
+
+            # Get the predicted class
+            predicted_ = np.argmax(prediction)
+
+            # Decode the prediction
+            if predicted_ == 0:
+                predicted_class = "Covid"
+            elif predicted_ == 1:
+                predicted_class = "Normal Chest X-ray"
+            else:
+                predicted_class = "Pneumonia"
+
+            st.image(image_pred, caption='Input image by user', use_column_width=True)
+            st.write("Prediction Classes for different types:")
+            st.write("COVID: 0")
+            st.write("Normal Chest X-ray: 1")
+            st.write("Pneumonia: 2")
+            st.write("\n")
+            st.write("DETECTED DISEASE DISPLAY")
+            st.write(f"Predicted Class : {predicted_}")
+            st.write(predicted_class)
+
+            # Analyze X-ray results with LLM
+            analyze_xray_with_llm(predicted_class)
+        else:
+            st.write("Please upload an image file or ensure the disease model is loaded.")
+
+    elif page == "Outbreak Alert":
+        st.markdown("## **Disease Outbreak News (from WHO)**")
+
+        # Fetch WHO news page
+        url = "https://www.who.int/news-room/events"
+        response = requests.get(url)
+        response.raise_for_status()  # Raise an exception for bad status codes
+
+        soup = BeautifulSoup(response.content, 'html.parser')
+
+        # Find news articles (adjust selectors if WHO website changes)
+        articles = soup.find_all('div', class_='list-view--item')
+
+        for article in articles[:5]:  # Display the top 5 news articles
+            title_element = article.find('a', class_='link-container')
+            if title_element:
+                title = title_element.text.strip()
+                link = title_element['href']
+                date_element = article.find('span', class_='date')
+                date = date_element.text.strip() if date_element else "Date not found"
+                
+                # Format date
+                date_parts = date.split()
+                if len(date_parts) >= 3:
+                    try:
+                        formatted_date = datetime.strptime(date, "%d %B %Y").strftime("%Y-%m-%d")
+                    except ValueError:
+                        formatted_date = date  # Keep the original date if formatting fails
+                else:
+                    formatted_date = date
+                
+                # Display news item in a card-like container
+                with st.container():
+                    st.markdown(f"**{formatted_date}**")
+                    st.markdown(f"[{title}]({link})")
+                    st.markdown("---")
+            else:
+                st.write("Could not find article details.")
+
+# Auto-scroll to the bottom of the chat container
+st.markdown(
+    """
+    <script>
+    const chatContainer = document.querySelector('.st-chat-container');
+    if (chatContainer) {
+        chatContainer.scrollTop = chatContainer.scrollHeight;
+    }
+    </script>
+    """,
+    unsafe_allow_html=True,
+)